深度学习作为数字信号处理设计的高效工具
1Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow, Russia. pryamikov@mail.ru.
Light, science & applications
|September 10, 2024
概括
在人工神经网络中广泛使用的反向传播算法为光学系统提供了高效的数字信号处理. 这种深度学习方法可以实现具有成本效益的,低复杂度的信号处理设计.
科学领域:
- 人工智能的人工智能
- 光学通信是指光学通信.
- 信号处理 信号处理
背景情况:
- 反向传播算法是人工神经网络训练的基石.
- 光纤传输系统需要高效的数字信号处理 (DSP) 方案.
- 目前的DSP设计可能面临复杂性和成本效益方面的挑战.
研究的目的:
- 在光纤传输系统中探索反向传播算法的应用.
- 调查用于DSP设计的深度学习框架的潜力.
- 为具有成本效益和低复杂度的DSP展示一个新的范式.
主要方法:
- 应用反向传播算法来开发DSP方案.
- 在DSP框架内利用深度学习原则.
- 评估拟议的DSP设计的效率和复杂性.
主要成果:
- 反向传播算法证明了在光纤系统中的DSP的有效性.
- 深度学习框架为DSP设计提供了一种新的方法.
- 开发的范式实现了高效率,低复杂性和成本.
结论:
- 基于反向传播的深度学习为光学DSP提供了一个强大的工具.
- 这种方法有助于创建先进,高效和经济的光学传输系统.
- 人工智能融入DSP标志着光通信技术的重大进步.
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